Spatial Ecology and Macroecology

Practical - Week 2

Florencia Grattarola

(Department of Spatial Sciences)

2022-10-10

What are we going to see today?

We will explore the potential drivers of species distribution.

Overview of data sources
Get data
1. Process raster
2. Visualise data
Correlations between predictors

worldclim.org

WorldClim is a database of high spatial resolution global weather and climate data. You can download gridded weather and climate data for historical (near current) and future conditions.

CHELSA (Climatologies at high resolution for the earth’s land surface areas) is a very high resolution (30 arc sec, ~1km) global downscaled climate data set. It includes climate layers for various time periods and variables, ranging from the Last Glacial Maximum, to the present, to several future scenarios.

nature.com/articles/s41597-020-00599-8

A global, spatially explicit characterization of 47 terrestrial habitat types, as defined in the International Union for Conservation of Nature (IUCN) habitat classification scheme.

Habitatmapping: https://github.com/Martin-Jung/Habitatmapping

ecoregions.appspot.com

The RESOLVE Ecoregions dataset, updated in 2017, offers a depiction of the 846 terrestrial ecoregions that represent our living planet.

land.copernicus.eu/pan-european/corine-land-cover

The CORINE Land Cover (CLC) consists of an inventory of land cover in 44 classes (from Europe). The inventory was initiated in 1985. Updates have been produced in 2000, 2006, 2012, and 2018.

modis.gsfc.nasa.gov

Terra MODIS and Aqua MODIS satellites are viewing the entire Earth’s surface every 1 to 2 days, acquiring data in 36 spectral bands or groups of wavelengths.

MODIStsp: https://github.com/ropensci/MODIStsp/

sedac.ciesin.columbia.edu

SEDAC, the Socioeconomic Data and Applications Center, focuses on human interactions in the environment and seeks to develop and operate applications that support the integration of socioeconomic and earth science data.

EXCERCISE Drivers

Drivers

In today’s practical we will focus in the Asian continent and assess five different layers:

Annual mean temperature (WorldClim BIO1)
Precipitation seasonality (WorldClim BIO15)
Elevation (WorldClim)
Land-cover (MODIS Terra MCD12Q1)
Human footprint index (SEDEC)

2. Get data: climatic

First, let’s get a base map of Asia using the rnaturalearth package.

library(rnaturalearth)
library(sf)
sf_use_s2(FALSE)
library(tidyverse)

Remember to install the package first.

2. Get data: climatic

First, let’s get a base map of Asia using the rnaturalearth package.

library(rnaturalearth)
library(sf)
sf_use_s2(FALSE)
library(tidyverse)

Asia <- ne_countries(scale=10,
                     continent='Asia',
                     type = 'map_units',
                     returnclass = 'sf')

2. Get data: climatic

Now, let’s dissolve the administrative borders using st_union()

Asia_union <- st_union(Asia) %>% st_cast()

2. Get data: climatic

Now, let’s dissolve the administrative borders using st_union().
And transform the layer to an equal area projection. We will use: WGS 1984 Lambert Asia.

Asia_union <- st_union(Asia) %>% st_cast()
Asia_union <- st_transform(Asia_union, crs='ESRI:102012')

2. Get data: climatic

Now, let’s dissolve the administrative borders using st_union().
And transform the layer to an equal area projection. We will use: WGS 1984 Lambert Asia.
Let’s store that proj4string in an object for later.

Asia_union <- st_union(Asia) %>% st_cast()
Asia_union <- st_transform(Asia_union, crs='ESRI:102012')

proj_Asia_ea <- '+proj=lcc +lat_0=0 +lon_0=105 +lat_1=30 +lat_2=62 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +type=crs'

2. Get data: climatic

Let’s see how it looks

2. Get data: climatic

Let’s download the WorldClim data (version 2.1 climate data for 1970-2000):

Annual mean temperature (bio1)
Annual precipitation (bio12)

You can download these data from https://www.worldclim.org/data/worldclim21.html